Kazembe, Lawrence, Clarke, Aileen, 1955- and Kandala, Ngianga-Bakwin. (2012) Childhood mortality in sub-Saharan Africa : cross-sectional insight into small-scale geographical inequalities from Census data. BMJ Open, Vol.2 (No.5). e001421. ISSN 2044-6055

Preview

Text WRAP_Kandala_Census_Paper2012.pdf - Published Version Download (661Kb) | Preview

Abstract

Objectives To estimate and quantify childhood mortality, its spatial correlates and the impact of potential correlates using recent census data from three sub-Saharan African countries (Rwanda, Senegal and Uganda), where evidence is lacking. Design Cross-sectional. Setting Nation-wide census samples from three African countries participating in the 2010 African Census round. All three countries have conducted recent censuses and have information on mortality of children under 5 years. Participants 111 288 children under the age of 5 years in three countries. Primary and secondary outcome measures Under-five mortality was assessed alongside potential correlates including geographical location (where children live), and environmental, bio-demographic and socioeconomic variables. Results Multivariate analysis indicates that in all three countries the overall risk of child death in the first 5 years of life has decreased in recent years (Rwanda: HR=0.04, 95% CI 0.02 to 0.09; Senegal: HR=0.02 (95% CI 0.02 to 0.05); Uganda: HR=0.011 (95% CI 0.006 to 0.018). In Rwanda, lower deaths were associated with living in urban areas (0.79, 0.73, 0.83), children with living mother (HR=0.16, 95% CI 0.15 to 0.17) or living father (HR=0.38, 95% CI 0.36 to 0.39). Higher death was associated with male children (HR=1.06, 95% CI 1.02 to 1.08) and Christian children (HR=1.14, 95% CI 1.05 to 1.27). Children less than 1 year were associated with higher risk of death compared to older children in the three countries. Also, there were significant spatial variations showing inequalities in children mortality by geographic location. In Uganda, for example, areas of high risk are in the south-west and north-west and Kampala district showed a significantly reduced risk. Conclusions We provide clear evidence of considerable geographical variation of under-five mortality which is unexplained by factors considered in the data. The resulting under-five mortality maps can be used as a practical tool for monitoring progress within countries for the Millennium Development Goal 4 to reduce under-five mortality in half by 2015.

Item Type:	Journal Article
Subjects:	H Social Sciences > HB Economic Theory H Social Sciences > HQ The family. Marriage. Woman R Medicine > R Medicine (General)
Divisions:	Faculty of Medicine > Warwick Medical School > Health Sciences
Library of Congress Subject Headings (LCSH):	Children -- Mortality -- Statistics, Children -- Mortality -- Africa, Sub-Saharan
Journal or Publication Title:	BMJ Open
Publisher:	BMJ
ISSN:	2044-6055
Date:	2012
Volume:	Vol.2
Number:	No.5
Page Range:	e001421
Identification Number:	10.1136/bmjopen-2012-001421
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access
References:	1. UNICEF. The State of the World’s children, 2010. Unicef Annual Report. 2. Becher H, Muller O, Jahn A, et al. Risk factors of infant and child mortality in rural Burkina Faso. Bull World Health Organ 2004;82:265–73. 3. Armstrong-Schellenberg JRM, Nathan R, Abdulla S, et al. Risk factors of child mortality in rural Tanzania. Trop Med Int Health 2002;7:506–11. 4. Binka FN, Indome F, Smith T. Impact of spatial distribution of permethrin-impregnated bednets on childhood mortality in rural northern Ghana. Am J Tropical Med Hygiene 1998;59:80–5. 5. Silverman JG, Decker MR, Wirth K, et al. Gender-based disparities in infant and child mortality based on maternal exposure to spousal violence: the heavy burden borne by Indian girls. Archiv Pediatric Adolesc Med 2011;165:22–7. 6. Kazembe L, Mpeketula PMG. Quantifying spatial disparities in neonatal mortality using a structured additive regression model. PLoS One 2010;5:e11180. 7. Johnson HL, Liu L, Fischer-Walker C, et al. Estimating the distribution of cuases of death among children age 1–59 months in high mortality countries with incomplete death certification. Int J Epidemiol 2010;39:1103–14. 8. Magadi MA. Household and community HIV/AIDS status and child malnutrition in sub-Saharan Africa: evidence from the demographic and health surveys. Soc Sc Med 2011;73:436–46. 9. Kandala NB, Fahrmeir L, Klasen S, et al. Geo-additive models of childhood undernutrition in three sub-Saharan African countries. Popul Space Place 2009;15:119–26. 10. de Sherbinin A. The biophysical and geographical correlates of child malnutrition in Africa. Popul Space Place 2011;17:27–46. 11. Wang L. Determinants of child mortality in LDCs: empirical findings from demographic and health surveys. Health Policy 2003;65:277–99. 12. Haines MR, Preston SH. The use of the census to estimate childhood mortality: comparisons from the 1900 and 1910 United States census public use samples. Hist Methods 1997;30:2. 13. Box-Steffensmeier JM, Jones BS. Event history modeling: a guide for social scientists. Cambridge, UK: Cambridge University Press, 2004. 14. Cox DR. Regression models and life-tables. JR Stat Soc Series B (Methodol) 1972;34:187–220. 15. Sastry N. A nested frailty model for survival data, with an application to the study of child survival in Northeast Brazil. J Am Stat Assoc 1997;92:426–35. 16. Bolstad WM, Manda SO. Investigating child mortality in Malawi using family and community random effects: a Bayesian analysis. J Am Stat Assoc 2001;96:12–19. 17. Kandala NB, Ghilagaber G. A geo-additive Bayesian discrete-time survival model and its application to spatial analysis of childhood mortality in Malawi. Quality & quantity–international. J Methodol 2006;40:935–57. 18. Adebayo SB, Fahrmeir L. Analysing child mortality in Nigeria with geoadditive discrete-time survival models. Stat Med 2005;24:709–28. 19. Caselli G, Pozzi L, Vaupel JW, et al. Family clustering in Sardinian longevity: a genealogical approach. Exp Gerontol 2006;41:727–36. 20. Hennerfeind A, Brezger A, Fahrmeir L. Geoadditive survival models. J Am Stat Assoc 2006;101:1065–75. 21. Hanagal DD. Modeling survival data using frailty models. Florida, USA: Chapman and Hall/CRC, 2011. 22. Vaupel JW, Manton KG, Stallard E. The impact of heterogeneity in individual frailty on the dynamics of mortality. Demography 1979;16:439–54. 23. Banerjee S, Wall MM, Carlin BP. Frailty modeling for spatially correlated survival data with application to infant mortality in Minnesota. Biostatistics 2003;4:123–42. 24. Bastos LS, Gamerman D. Dynamic survival models with spatial frailty. Lifetime Data Anal 2006;12:441–60. 25. Li Y, Ryan L. Modeling spatial survival data using the semi-parametric frailty models. Biometrics 2002;58:287–97. 26. Gemperli A, Vounatsou P, Kleinschmidt I, et al. Spatial patterns of infant mortality in Mali: the effect of malaria endemicity. Am J Epidemiol 2004;159:64–72. 27. Escarams G, Carrasco JL, Aponte JJ, et al. Spatio-temporal analysis of mortality among children under the age of five in Manhia (Mozambique) during the period 1997–2005. Int J Health Geogr 2011;10:14. 28. Kamman EE, Wand MP. Geoadditive models. J R Statist Soc B 2003;53:118. 29. Kneib T, Fahrmeir L. A mixed model approach for geoadditive hazard regression. Scandinavian J Statist 2007;34:207228. 30. Brezger A, Kneib T, Lang S, et al. Software for Bayesian inference based on Markov Chain Monte Carlo simulation techniques. J Statist Software 2005;14:11. 31. Brooks SP, Gelman A. General methods for monitoring convergence of iterative simulations. J Comp Graph Statist 1998;7:434–55. 32. Gould JB, Herrchen B, Pham T. Small-area analysis: targeting high-risk areas for adolescent pregnancy prevention programs. Fam Plan Perspect 1998;30:173176. 33. Manda SOM, Feltbower RG, Gilhorpe MS. A multivariate random effects model for multiple spatially dependent survival data. In: Tu Y, Greenwood D, eds. Modern methods for epidemiology. Dordrecht: Springer, 2012:157–72. 34. Browne WJ, Goldstein H, Rasbash J . Multiple membership multiple classification models. Stat Model 2001;1:103–24. 35. Zhou L, Hanson TE. Spatially dependent Polya tree modeling of survival data. Biometrics 2011;67:391–403. 36. Carlin BP, Banerjee S, Wall M. Frailty modelling for spatially correlated survival data with application to infant mortality in Minnesota. Biostatistics 2003;4:123–42. 37. Gastafson P. Large hierarchical Bayesian analysis of multivariate survival data. Biometrics 1997;53:230–42. 38. Spiegelhalter DJ, Best NG, Carlin BP, et al. Bayesian measures of model complexity and fit (with discussion). J R Stat Soc B 2002;64:1–34.
URI:	http://wrap.warwick.ac.uk/id/eprint/51185

Request changes to a record

Actions (login required)

View Item